Success rates for a passer and an interceptor in different cases
\n\n
\n\nThe project work was funded by the European Commission (EC) 7th Framework Programme (FP7), under the 9th Call for projects on Information and Communication Technologies. The publishing of this book was funded by the EC FP7 Post-Grant Open Access Pilot programme. 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Nguyen received his M.A. in Mathematics, and his Ph.D. in Applied Mathematics from the Claremont Graduate University; M.S.E.E. in Communication Systems Theory from University of California San Diego; and B.S.E. in Electronics and M.S.E. in Electromagnetic Field Theory from California State University Fullerton (CSUF). He also completed all course requirements and passed the comprehensive exam for his M.S.E.E. in Digital Signal Processing from California State University Long Beach. Dr. Nguyen is an expert in Satellite Operations (SATOPS), Satellite Communications (SATCOMs), advanced mathematical modeling for complex systems-of-systems, sensing and communication networks.\nCurrently, he serves as Adjunct Research Professor at CSUF, Mathematics Dept. Concurrently, he is also with the Aerospace Corporation, serving as a Deputy Chief System Architect in Space Systems Architect, Global Partnerships Subdivision. He has more than 13-years of service at Aerospace, and prior to his current position; he has served as Sr. Engineering Specialist, Sr. Project Lead, Section Manager, Associate Director, Interim Director, and Principal Technical Staff (the highest technical level at the corporation). At Aerospace, he invented HPA linearizer, GMSK synchronizers and developed advanced optimization techniques using game theory for achieving affordable and low-risk acquisition strategy. Prior to CSUF, he had also held a Research Assistant Professor at the Catholic University of America in concurrent with The Aerospace Corporation positions. \nHe was a Engineering Fellow from Raytheon, where he had 10-year of services at Raytheon, serving as Program Area Chief Engineer, Program Chief Engineer, PI, Technical Director, Program Manager, Lead Architect and Lead System Engineer for many advanced programs and pursuits related to sensing and communication networks. At Raytheon, he invented radar-communication technology and gun barrel detector using millimeter-wave. Previous to Raytheon and Aerospace Corporation, Dr. Nguyen was with NASA/JPL for more than 11-years, where he served as the NASA delegate to the international Consultative Committee for Space Data System (CCSDS). Many of his works on RF and Modulation were adopted as the CCSDS standards for USB waveforms and space RF systems. At JPL he invented QPSK phase ambiguity resolver and developed innovative optimization technique for simultaneous range-command-telemetry operation. He built the first laser lab and automated manufacturing lab when he was with ITT Technical Services in the early ’80s. \nHe has published more than 250 technical reports and papers. His work has appeared in NASA TechBrief, textbook, Open Access Book, SIAM Publication, CCSDS Blue Book, and Wiley & Sons Encyclopedia of Electrical and Electronics Engineering. He was selected as a Vietnamese-American Role Model by KCSI-TV, Channel 18 in 2002, and Recognition Honoree at 50-Year Celebration of CSUF in 2007. He received numerous Raytheon, Aerospace and NASA awards, and Air Force commendations. He holds 16 patents and has 01 patent pending. His biography has been listed in Marquis Who’sWho in Science and Engineering in America.",institutionString:"The Aerospace Corporation",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"California State University, Fullerton",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:[{id:"72485",title:"Satellite Control System: Part I - Architecture and Main Components",slug:"satellite-control-system-part-i-architecture-and-main-components",totalDownloads:157,totalCrossrefCites:0,authors:[null]},{id:"72443",title:"Effective Algorithms for Detection Outliers and Cycle Slip Repair in GNSS Data Measurements",slug:"effective-algorithms-for-detection-outliers-and-cycle-slip-repair-in-gnss-data-measurements",totalDownloads:71,totalCrossrefCites:0,authors:[null]},{id:"72147",title:"Future Satellite System Architectures and Practical Design Issues: An Overview",slug:"future-satellite-system-architectures-and-practical-design-issues-an-overview",totalDownloads:132,totalCrossrefCites:0,authors:[{id:"210657",title:"Dr.",name:"Tien",surname:"Nguyen",slug:"tien-nguyen",fullName:"Tien Nguyen"}]},{id:"72340",title:"Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors",slug:"game-theoretic-training-enabled-deep-learning-solutions-for-rapid-discovery-of-satellite-behaviors",totalDownloads:101,totalCrossrefCites:0,authors:[null]},{id:"72725",title:"Communication Subsystems for Satellite Design",slug:"communication-subsystems-for-satellite-design",totalDownloads:239,totalCrossrefCites:0,authors:[null]},{id:"72839",title:"Overview of Existing and Future Advanced Satellite Systems",slug:"overview-of-existing-and-future-advanced-satellite-systems",totalDownloads:58,totalCrossrefCites:0,authors:[null]},{id:"72620",title:"Dynamic Link from Liftoff to Final Orbital Insertion for a MEO Space Vehicle",slug:"dynamic-link-from-liftoff-to-final-orbital-insertion-for-a-meo-space-vehicle",totalDownloads:58,totalCrossrefCites:0,authors:[null]},{id:"72311",title:"System Designs of Microsatellites: A Review of Two Schools of Thoughts",slug:"system-designs-of-microsatellites-a-review-of-two-schools-of-thoughts",totalDownloads:154,totalCrossrefCites:0,authors:[null]},{id:"72742",title:"Design of Intelligent and Open Avionics System Onboard",slug:"design-of-intelligent-and-open-avionics-system-onboard",totalDownloads:48,totalCrossrefCites:0,authors:[null]},{id:"74274",title:"Analysis of Spatiotemporal Variability of Surface Temperature of Okhotsk Sea and Adjacent Waters Using Satellite Data",slug:"analysis-of-spatiotemporal-variability-of-surface-temperature-of-okhotsk-sea-and-adjacent-waters-usi",totalDownloads:35,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"205697",firstName:"Kristina",lastName:"Kardum Cvitan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/205697/images/5186_n.jpg",email:"kristina.k@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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A wide range of applications, such as forage robots (Mataric, 1997), soccer playing robots (Asada et al., 1996), prey-pursuing robots (Fujii et al., 1998) and so on, have been investigated. However, a straightforward application of the simple reinforcement learning method to multi-robot dynamic systems has a lot of issues, such as uncertainty caused by others, distributed control, partial observability of internal states of others, asynchronous action taking, and so on. In this paper we mainly focus on two major difficulties in practical use :
\n\t\t\tunstable dynamics caused by policy alternation of other agents
curse of dimension problem
The policy alternation of others in multi-agent environments may cause sudden changes in state transition probabilities of which constancy is needed for behavior learning to converge. Asada et al. (Asada et al., 1999) proposed a method that sets a global learning schedule in which only one agent is specified as a learner with the rest of the agents having fixed policies to avoid the issue of the simultaneous learning. As a matter of course, they did not consider the alternation of the opponent’s policies. Ikenoue et al. (Ikenoue et al., 2002) showed simultaneous cooperative behavior acquisition by fixing learners’ policies for a certain period during the learning process. In the case of cooperative behavior acquisition, no agent has any reason to change policies while they continue to acquire positive rewards as a result of their cooperative behavior with each other. The agents update their policies gradually so that the state transition probabilities can be regarded as almost fixed from the viewpoint of the other learning agents. Kuhlmann and Stone (Kuhlmann and Stone, 2004) have applied a reinforcement learning system with a function approximator to the keep-away problem in the situation of the RoboCup simulation league. In their work, only the passer learns his policy is to keep the ball away from the opponents. The other agents (receivers and opponents) follow fixed policies given by the designer beforehand.
\n\t\t\tThe amount of information to be handled in multi-agent system tends to be huge and easily causes the curse of dimension problem. Elfwing et al.(Elfwing et al., 2004) achieved the cooperative behavior learning task between two robots in real time by introducing the macro action that is an abstracted action code predefined by the designer. However, only the macro actions do not seem sufficient to accelerate the learning time in a case that more agents are included in the environment. Therefore, the sensory information should be also abstracted to reduce the size of the state space. Kalyanakrishnan et al. (Kalyanakrishnan et al., 2006) showed that the learning rate can be accelerated by sharing the learned information in the 4 on 5 game task. However, they still need long learning time since they directly use the raw sensory information as state variables to determine the situation that the learning agent encounters.
\n\t\t\tKeys for cooping with the above difficulties are to divide a whole complex situation into several ones in which state transition can be regarded as stable enough, and to keep exploration space as small as possible based on abstracted task specific information instead of the row sensory information. A modular learning system might be a practical solution for those difficulties.
\n\t\t\tThis chapter briefly introduces examples of application of modular learning systems for cooperative/competitive behavior acquisition in scenarios of RoboCup Middle Size League. A modular learning system is successfully applied for adaptation to the policy alternation of others by switching modules each of which corresponds to different situation caused by the policy alternation of the other. Introduction of macro actions enables reduction of exploration space and simultaneous multi-agent behavior learning. The experimental results of 2 on 3 passing task are shown. Furthermore, in order to attack the problem of curse of dimension, a state abstraction method based on state value function of a behavior learning module is proposed and applied to the 4 on 5 passing task. A player can acquire cooperative behaviors with its teammates and competitive ones against opponents within a reasonable learning time. Finally, conclusions and future work are shown.
\n\t\tIn this section, a modular learning system for behavior acquisition in the multiagent environment is introduced. A multi-module learning system for even single agent learning in a multi-agent environment is shown difficult when we straightforwardly apply it. A simple learning scheduling is introduced in order to make it relatively easy to assign modules automatically. Second, macro actions are introduced to realize simultaneous learning in multi-agent environments in which each agent does not need to fix its policy according to some learning schedule. More detailed description was given in (Takahashi et al., 2005).
\n\t\t\tBefore describing the modular learning system in details, a task in the RoboCup middle size league context is introduced as a testbed to evaluate the learning system. The game is like a three-on-one involving one opponent and three other players. The player nearest to the ball becomes a passer who passes the ball to one of its teammates (receivers) while the opponent tries to intercept it. Fig.2 shows the viewer of our simulator for the robots and the environment and a situation the learning agents are supposed to encounter. Fig.1 shows a mobile robot we have designed and built. The robot has an omni-directional camera system. A simple color image processing is applied to detect the ball, the interceptor, and the receivers on the image in real-time (every 33ms.) The left of Fig.2 shows a situation a learning agent can encounter while the right images show the simulated ones of the normal and omni vision systems. The mobile platform is an omni-directional vehicle (roration and translation in any direction on the plane are possible at any moment).
\n\t\t\t\tA real Robot
A 3 on 1 game (left) and the viewer of the game simulator (right)
The basic idea is that the learning agent could assign one behavior learning module to one situation which reflects another agent’s behavior and the learning module would acquire a purposive behavior under the situation if the agent can distinguish a number of situations, each in which the state transition probabilities are almost constant. We introduce a modular learning approach to realize this idea (Fig.3). A module consists of both a learning component that models the world and an action planner. The whole system follows these procedures:
\n\t\t\t\tselect a module in which the world model is estimated best among the modules;
update the model in the module; and
calculate action values to accomplish a given task based on the estimated model using dynamic programming.
Adaptive behavior selection based on Multi-module learning system
As an experimental task, we suppose ball passing with the possibility of being intercepted by the opponent (Fig.2). The problem for the passer (interceptor) here is to select one module of which model can most accurately describe the interceptor’s (passer’s) behavior from the viewpoint of the agent and then to take an action based on the policy which is planned with the estimated model.
\n\t\t\t\tA multi-module learning system
\n\t\t\t\t\tFig. 4. shows a basic architecture of the proposed system, i.e., a modular reinforcement learning system. Each module has a forward model (predictor) which represents the state transition model and a behavior learner (action planner) which estimates the state-action value function based on the forward model in a reinforcement learning manner. This idea of a combination of a forward model and a reinforcement learning system is similar to the H-DYNA architecture (Singh, 1992) or MOSAIC (Doya et al., 2000). The system selects one module which has the best estimation of a state transition sequence by activating a gate signal corresponding to the module while deactivating the gate signals of the other modules; the selected module then sends action commands based on its policy.
\n\t\t\tFirst, we show how it is difficult to directly introduce the proposed multi-module learning system in the multi-agent system. A simple learning scheduling is introduced in order to make it relatively easy to assign modules automatically.
\n\t\t\t\tThe initial positions of the ball, passer, interceptor, and receivers are shown in Fig. 2. The opponent has two kinds of behaviors: it defends the left side or right side. The passer agent has to estimate which direction the interceptor will defend and go to the position so as to kick the ball in the direction the interceptor does not defend. From the viewpoint of the multi-module learning system, the passer will estimate which situation of the module is going on and select the most appropriate module as its behavior. The passer acquires a positive reward when it approaches the ball and kicks it to one of the receivers.
\n\t\t\t\tA learning schedule is composed of three stages to show its validity. The opponent fixes its defending policy as a right-side block at the first stage. After 250 trials, the opponent changes the policy to block the left side at the second stage and continues this for another 250 trials. Finally, the opponent changes the defending policy randomly after one trial.
\n\t\t\tThe state space is constructed in terms of the centroid of the ball on the image, the angle between the ball and the interceptor, and the angles between the ball and the potential receivers (see Figs. 9 (a) and (b)). The action space is constructed in terms of the desired three velocity values (xd, yd, wd) to be sent to the motor controller (Fig. 6). The robot has a pinball-like kick device which allows it to automatically kick the ball whenever the ball comes within the region to be kicked. It tries to estimate the mapping from sensory information to appropriate motor commands by the proposed method.
\n\t\t\t\tState variables : Left : (a) state variables (position) Right: (b) state variables (angle)
Action variables
We have applied the method to a learning agent and compared it with only one learning module. The performances between the methods with and without the learning scheduling are compared as well. Fig.7 shows the success rates of those during the learning process. ”success” indicates the learning agent successfully kicked the ball without interception by the opponent. The success rate shows the number of successes in the last 50 trials. The “mono. module” in the figure means a “monolithic module” system which tries to acquire a behavior for both policies of the opponent.
\n\t\t\t\tSuccess rates during the learning
The multi-module system with scheduling shows a better performance than the one-module system. The monolithic module with scheduling means we applied the learning scheduling mentioned in 2.3 even though the system has only one learning module. The performance of this system is similar to the multi-module system until the end of the first stage between the fist and the 250th trials; however, it goes down at the second stage because the obtained policy is biased by the experiences at the first stage and cannot follow the policy change of the opponent. Because the opponent uses one of the policies at random in the third stage, the learning agent obtains about 50% of the success rate.
\n\t\t\t\tThe term “without scheduling” means we do not apply learning scheduling and the opponent changes its policy at random from the beginning. Somehow the performance of the monolithic module system without learning scheduling gets worse after 200 trials. The multi-module system without a learning schedule shows the worst performance in our experiments. This result indicates it is very difficult to recognize the situation at the early stage of the learning process because the modules have too few experiences to evaluate their fitness; thus, the system tends to select the module without any consistency. As a result, the system cannot acquire any valid policies.
\n\t\t\tThe exploration space with macro actions becomes much smaller than the one with primitive actions; therefore, the macro action increases the possibility of creating cooperative/competitive experiences and leads the two agents to find a reasonable solution in a realistic learning time frame. Here, macro actions are introduced in order to realize simultaneous learning in a multi-agent environment in which each agent does not need to fix its policy according to some learning schedule. In this experiment, the passer and the interceptor learn their behaviors simultaneously. The passer learns behaviors for different situations caused by the alternation of the interceptor’s policies, i.e., blocking to the left side or the right. The interceptor also learns behaviors for different situations caused by the alternation of the passer’s policies, i.e., passing a ball to a left receiver or a right one.
\n\t\t\t\n\t\t\t\t\tFig. 8 shows the macro actions of the passer and the interceptor. The macro actions by the interceptor are blocking the pass way to the left receiver and the right one. On the other hand, the macro action by the passer are turning left, turning right around the ball, and approaching the ball to kick it. A ball gazing control is embedded in both learners. The number of the actions is 2 and 3, respectively.
\n\t\t\t\tMacro actions
State variables Left : (a) passer Right : (b) interceptor
The state space for the passer is constructed in terms of the y position of the ball on the normal image, the angle between the ball and the centers of interceptor, and the angles between the balls and the two receivers on the image of omni-directional vision. The number of the states is reduced because the set of macro actions enable us to select a smaller number of state variables and coarser quantization. The state space for the interceptor is constructed in terms of the y position of the passer on the image of normal vision system, the angle between the ball and the passer, and the angles between the ball and the two receivers on the image of omni-directional vision.
\n\t\t\tWe have checked how the simultaneous learning of the passer and interceptor works on our computer simulation. Both agents start to learn their behaviors from scratch and have 1500 trials without any scheduling. To check whether both learners acquired appropriate behaviors against the opponent’s behaviors, we fixed one agent’s policy and checked to see if the other could select an appropriate behavior, then determined its success rate. Table 1 shows these results.
\n\t\t\t\tPasser | \n\t\t\t\t\t\t\tInterceptor | \n\t\t\t\t\t\t\tPassers success rate [%] | \n\t\t\t\t\t\t\tInterceptors success rate [%] | \n\t\t\t\t\t\t\tDraw rate [%] | \n\t\t\t\t\t\t
LM0, LM1 | \n\t\t\t\t\t\t\tLM0 | \n\t\t\t\t\t\t\t59.0 | \n\t\t\t\t\t\t\t23.0 | \n\t\t\t\t\t\t\t18.0 | \n\t\t\t\t\t\t
LM0,LM1 | \n\t\t\t\t\t\t\tLM1 | \n\t\t\t\t\t\t\t52.7 | \n\t\t\t\t\t\t\t34.3 | \n\t\t\t\t\t\t\t13.0 | \n\t\t\t\t\t\t
LM0 | \n\t\t\t\t\t\t\tLM0,LM1 | \n\t\t\t\t\t\t\t25.6 | \n\t\t\t\t\t\t\t55.0 | \n\t\t\t\t\t\t\t19.4 | \n\t\t\t\t\t\t
LM1 | \n\t\t\t\t\t\t\tLM0,LM1 | \n\t\t\t\t\t\t\t26.0 | \n\t\t\t\t\t\t\t59.3 | \n\t\t\t\t\t\t\t14.7 | \n\t\t\t\t\t\t
LM0,LM1 | \n\t\t\t\t\t\t\tLM0,LM1 | \n\t\t\t\t\t\t\t27.6 | \n\t\t\t\t\t\t\t37.3 | \n\t\t\t\t\t\t\t25.1 | \n\t\t\t\t\t\t
Success rates for a passer and an interceptor in different cases
Both players have two modules and were assigned to appropriate situations by themselves. LM and the digit number right after the LM indicate the Learning Module and the index number of the module, respectively. For example, if the passer uses both LM0 and LM1 and the interceptor uses only LM0, then the passer’s success rate, interceptor’s success rate, and draw rate are 59.0 %, 23.0%, and 18.0%, respectively. Apparently, the player with multi-modules switching achieves a higher success rate than the opponent using only one module. These results demonstrate the multi-module learning system works well for both.
\n\t\t\t\tThe same architecture is applied to the real robots. Fig. 10 shows one example of behaviors by real robots. First, the interceptor tried to block the left side, then the passer approached the ball with the intention of passing it to the right receiver. The interceptor found it was trying to block the wrong side and changed to block the other (right) side, but it was too late to intercept the ball and the passer successfully passed the ball to the right receiver.
\n\t\t\t\tA sequence of a behavior of passing a ball to the right receiver while
Conventional approaches, including ones described in the previous sections, have been suffering from the curse of dimension problem when they are applied to multiagent dynamic environments. State/action spaces based on sensory information and motor commands easily become too huge for a learner to explore. In the previous section, macro actions are introduced to reduce the exploration space and enable agents to learn purposive competitive behaviors according to the situation caused by the opponent. As the next step, state space should be constructed as small as possible to enable cooperative/competitive behaviour learning in practical time. The key ideas to resolve the issue are as follows. First, a two-layer hierarchical system with multi learning modules is adopted to reduce the size of the sensor and action spaces. The state space of the top layer consists of the state values from the lower level, and the macro actions are used to reduce the size of the physical action space. Second, the state of the other to what extent it is close to its own goal is estimated by observation and used as a state value in the top layer state space to realize the cooperative/competitive behaviors. The method is applied to 4 (defense team) on 5 (offense team) game task, and the learning agent successfully acquired the teamwork plays (pass and shoot) within much shorter learning time. Here, the method is briefly introduced. More detailed description was given in (Noma et al., 2007).
\n\t\t\t\n\t\t\t\tFig.11 shows a basic architecture of the proposed system, i.e., a two-layered multi-module reinforcement learning system. The bottom layer consists of two kinds of modules: behavior modules and other’s state value estimation ones. The top layer consists of a single gate module that learns which behavior module should be selected according to the current state that consists of state values sent from the modules at the bottom layer. The gate module acquires a purposive policy to select an appropriate behavior module based on reinforcement learning.
\n\t\t\tThe role of the other’s state value estimation module is to estimate the state value that indicates the degree of achievement of the other’s task through observation, and to send this value to the state space of the gate module at the top layer. In order to estimate the degree of achievement, the following procedure is taken.
\n\t\t\tThe learner acquires the various kinds of behaviors that the other agent may take, and each behavior corresponds to each behavior module that estimates state value of the behavior.
The learner estimates the sensory information observed by the other through the 3-D reconstruction of its own sensory information.
Based on the estimated sensory information of the other, each other’s state value estimation module estimates the other’s state value by assigning the state value of the corresponding behavior module of its own.
A multi-module learning system
The game consists of the offense team (five players and one of them can be the passer) and the defense team (four players attempt to intercept the ball). The offense player nearest to the ball becomes a passer who passes the ball to one of its teammates (receivers) or shoot the ball to the goal if possible while the opposing team tries to intercept it (see Fig. 12).
\n\t\t\t\tA passer and the defense formation
Only the passer learns its behavior while the receivers and the defense team members take the fixed control policies. The receiver becomes the passer after receiving the ball and the passer becomes the receiver after passing the ball. After one episode, the learned information is circulated among team members through communication channel but no communication during one episode. The behavior and the state value estimation modules are given a priori. The offense (defense) team color is magenta (cyan), and the goal color is blue (yellow) in the following figures.
\n\t\t\t\tThe passer who is the nearest to the ball passes the ball to one of four receivers or dribble-shoots the ball to the goal. After its passing, the passer shows a pass-and-go behavior that is a motion to the goal during the fixed period of time. The receivers face to the ball and move to the positions so that they can form a rectangle by taking the distance to the nearest teammates (the passer or other receivers) (see Fig. 12). The initial positions of the team members are randomly arranged inside their territory.
\n\t\t\t\tThe defense team member who is nearest to the passer attempts to intercept the ball, and each of other members attempts to “block” the nearest receiver.“Block” means to move to the position near the offense team member and between the offense and its own goal (see Fig. 12). The offense team member attempts to catch the ball if it is approaching. In order to avoid the disadvantage of the offense team, the defense team members are not allowed inside the penalty area during the fixed period of time. The initial positions of the team members are randomly arranged inside their territory but outside the center circle.
\n\t\t\tThe passer is only one learner, and the state and action spaces for the lower modules and the gate one are constructed as follows. The action modules are four passing ones for four individual receivers, and one dribble-shoot module. The other’s state value estimation modules are the ones to estimate the degree of achievement of ball receiving for four individual receivers, that is how easily the receiver can receive the ball from the passer. These modules are given in advance before the learning of the gate module.
\n\t\t\t\tThe action spaces of the lower modules adopt the macro actions that the designer specifies in advance to reduce the size of the exploration space without searching at the physical motor level. The state space S for the gate module consists of the following state values from the lower modules:
\n\t\t\t\tfour state values of passing behavior modules corresponding to four receivers,
one state value of dribble-shoot behavior module, and
four state values of receiver’s state value estimation modules corresponding to four receivers.
In order to reduce the size of the whole state space, these values are binarized, therefore its size is 24 x 2 x 24=512.
The rewards are given as follows:
10 when the ball is shot into the goal (one episode is over),
1 when the ball is intercepted (one episode is over),
when the ball is successfully passed,
when the ball is dribbled.
When the ball is out of the field or the pre-specified time period elapsed, the game is called “draw” and one episode is over.
The success rate is shown in Fig. 13(a) where the action selection is 80% greedy and 20% random to cope with new situations. Around the 900th trial, the learning seems to have converged at 30% success, 70% failure, and 10% draw. Compared to the results of (Kalyanakrishnan et al., 2006) that has around 30% success rate with 30,000 trials, the learning time is drastically improved (30 times quicker). Fig. 13(b) indicates the number of passes where it decreases after the 350 trials that means the number of useless passes decreased.
\n\t\t\t\tIn cases of the success, failure, and draw rates when 100% greedy and 100% random are 55%, 35%, 10%, and 2%, 97%, 1%, respectively. The reason why the success rate in case of 100% greedy is better than in case of 80% greedy seems that the control policies of the receivers and the defense players are fixed, therefore not so new situations happened.
\n\t\t\t\tAn example of acquired behavior is shown in Fig. 14 where a sequence of twelve top views indicates a successful pass and shoot scene.
\n\t\t\t\ta) Success rates and (b) the number of passes
An example of the acquired behavior in 5 on 4 game
Although we have not used the communication between agents during one episode, the receiver’s state value estimation modules seem to take the similar role. Then, we performed the learning without these modules. Fig. 15 shows the success rate, and we can see that the converged success rate is around 21% that is close to 23% of the success rate of the result of the existing method (Kalyanakrishnan et al., 2006).
\n\t\t\t\tSuccess rate without the receiver’s state value estimation modules
In this chapter, we have showed a method by which multiple modules are assigned to different situations caused by the alternation of the other agent’s policy so that an agent may learn purposive behaviors for the specified situations as consequences of the other agent’s behaviors.
\n\t\t\tMacro actions are introduced to realize simultaneous learning of competitive behaviors in a multi-agent system. Results of a soccer situation and the importance of the learning scheduling in case of none-simultaneous learning without macro actions, as well as the validity of the macro actions in case of simultaneous learning in the multi-agent system, were shown.
\n\t\t\tWe have also showed another learning system using the state values instead of the physical sensor values and macro actions instead of the physical motor commands, and adopted the receiver’s state value estimation modules that estimate how easy for each receiver to receive the ball in order to accelerate the learning. The state and action space abstraction (the use of state values and macro actions) contributes to the reduction of the learning time while the use of the receiver’s state value estimation modules contributed to the improvement of the teamwork performance.
\n\t\tStroke also known as cerebrovascular accidents is the world’s second death-perpetrating disease after cardiovascular diseases [1, 2], and it affects about 13.7 million people annually in the globe [3]. About one third of all strokes translate into fatalities, and another one third constitutes stroke survivors staying with residual disability that accounts as foremost noticeable root of long-term neurological disability in adults [4, 5] and third most common cause of all disabilities globally [6]. Stroke classically depicts a syndrome with sudden onset of acute focal injury of the central nervous system (CNS) of vascular origin that produces focal or global neurological deficit in accordance with affected area of blood supply [7]. Thus, based on the isolated territory of the brain involve, stroke can be cerebral stroke, brainstem stroke, cerebellar stroke, or thalamic stroke, while based on underline cause it can be ischemic stroke (thrombotic, embolic, lacunar, watershed, or cryptogenic) which results from brain vascular occlusion, or hemorrhagic stroke (intraparenchymal or subarachnoid) which is due to blood-related aberrations [8].
Cerebral stroke results in loss of cerebral cortex related functions that manifests as motor impairment [9, 10, 11], sensory impairment [12, 13, 14], cognitive impairment [15, 16, 17], balance impairment [18] among others. The motor function of the cerebral cortex is embedded in the motor cortex (primary motor area, premotor cortex, supplementary motor area, cingulate motor areas) located in the frontal lobe anterior to central sulcus, the motor cortex is responsible for planning, initiation, execution, and regulation of voluntary movement which is achieved through originating descending corticospinal tract and corticobulbar system to the spinal cord and brainstem respectively [19]. Cerebral cortex plays principal role in sensory/perceptual functions by providing meaning to all sensations (except sense of smell) through primary somatosensory cortex in the postcentral gyrus of the parietal lobe, and other primary cortical sensory areas such as auditory cortex in the temporal lobe and visual cortex in the occipital lobe. Cognitive function involves multifaceted domains of cognitive processes including memory, learning, attention, thought, comprehension, perception, language among others [20]. Each of these domains of cognition requires cerebral cortex, illustration can be seen in memory domain where memory acquisition involves sensory cortex, memory retrieval involves prefrontal cortex, and memory storage is distributed throughout the cortex [21]. Balance and coordination of movement involve integrated functioning of both pyramidal and extra-pyramidal systems, and the cerebral cortex is the main principal origin of pyramidal system.
The mechanism of cerebral damage after stroke determines the cerebral stroke impairments, and the mechanism of damage is relative to whether the type of stroke is ischemic or hemorrhagic. Ischemic stroke consists of five distinct pathophysiologic mechanism each of which has distinct time frame; these includes immediate (within minutes) peri-infarct depolarization and excitotoxicity, hours later by neuro-inflammation and oxidative stress, days later by apoptosis [8]. In addition to ischemia related cascade of events aforementioned, hemorrhagic stroke is associated with two additional unique pathophysiologic phases. The primary; acute phase which is due to physical effect of hematoma (mass effect) from the mass accumulated blood, and the secondary; subacute phase termed as cytotoxicity from secondary metabolites of blood components [22, 23, 24].
Recovery to some extent from post stroke impairments observed among stroke survivors was one of the early evidences that led to move away from outdated dogma widely misconceived previously that; there was no possibility for repair or change within the CNS after it had suffered a lesion; and that once there is damage such as stroke that leads to neuronal demise inadvertently, the brain structures and functions are lost forever [25, 26]. It is now well-established fact that CNS repair or change itself but it just that it relatively does not do well enough, and that functional recovery after damage relies on neuroplasticity [27, 28]. Neuroplasticity is life-long natural capability of the CNS to rearrange itself in both molecular form and function in response to new experience or stimulus. Brain plasticity is pivotal to functional recovery after cerebral stroke, and this spontaneous, endogenous and intrinsic capacity of the brain is what restorative rehabilitation approaches for stroke explore, promote and remodel in the right direction to achieve optimal functional recovery after stroke [29, 30].
There is exploding surge among scientists to pay more attention in searching for various therapeutic strategies that can enhance neuroplasticity to augment functional recovery with rehabilitation after stroke [31, 32, 33, 34]. Although this strategy is still in developmental stage but the reasons for this shift in attention are not far-fetched. Firstly, the thrombolytic/thrombectomy clinical treatment available for acute stroke has a very restrictive time window of administration of 4–5 hours of lesion onset [35]. This is in contrast to restorative/rehabilitative interventions that has unlimited therapeutic window of lifelong applicability [36]. Secondly, rehabilitation interventions are still far from sufficiency for optimal and ideal recovery from impairments after stroke [37], as about 50% of stroke survivors still leaves with residual disability and remain functionally dependent despite rehabilitative management [38]. Understanding the mechanisms of cerebral damage and their recovery after cerebral stroke is essential towards development of strategies that harness and enhance neuroplasticity in combination with rehabilitation processes [39]. This paper therefore discusses the mechanism of cerebral damage after stroke as well as elucidates the concept of neuroplasticity as key for recovery following stroke.
In ischemic stroke, irreversible cascade of damage to the brain tissue ensue once the cerebral blood flow (CBF) reduces to less than 12 ml/100 g/min of the normal range of 50–60 ml/100 g/min. Within seconds of this abrupt ischemic insult, neuronal cells in the center of ischemic region termed as ischemic prenumbra undergoes anoxic depolarization due to loss of ATP-dependent ionic pump homeostasis, and they never repolarize [40]. This necrotic core of ischemic prenumbra is enclosed by a zone of relatively lesser impacted tissue termed as ischemic penumbra, which is abridged functionally silent by the reduced blood flow but maintains metabolically active and therefore can repolarize at the expense of further energy consumption [41]. This repetitive depolarization and repolarization of ischemic penumbra are termed peri-infarct depolarization and the important period of time during which this volume of brain tissue is salvageable is referred to as the window of opportunity. The energy failure in the functioning of ATP dependent sodium potassium pump in the ischemic prenumbra results in massive uncontrolled anoxic depolarization that results in opening of voltage-gated calcium channels, mitochondrial dysfunction which further deplete energy required to maintain ion gradient, and abnormally extracellular buildup of excitatory amino acids [42, 43].
Consequently, excitatory glutamate and other excitatory amino acids such as aspartate becomes excessively released, and glutamate hyperexcitation of glutamate N-methyl-D-aspartate (NMDA) receptor, which is arguably the most calcium-influx allowing ionotropic glutamate receptor; results in massive influx of calcium ion (Ca++) into hypoxic neuron. Calcium ion triggers series of cascading events that ultimately lead to neuronal demise through activation of proteolytic enzymes, stimulation of pathogenic genes, lipid peroxidation and free radical generation [44]. For this; glutamate and other excitatory amino acids are cumulatively termed excitotoxins, and their accompanying neuronal damage termed excitotoxicity [45]. Calcium activates key number of disparaging intracellular enzymes such as proteases, kinases, lipases, and endonuclease that not only wildly permits release of cytokines and other mediators that result in the loss of cellular integrity but also orchestrated triggering of intrinsic apoptotic pathway of neuronal death. Specifically, calcium through mobilizing phospholipases hydrolyses membrane bound glycerophospholipids to yield free fatty acids, which enable free radical peroxidation of other membrane bound lipids. Calcium through mobilizing proteases lyses integral structural proteins and activates nitric oxide synthase enzyme that triggers free radical machinery [46].
Prior excitotoxicity activates microglia and astrocytes which are the brain resident innate immunity to reacts and release cytokines, chemokines (chemotaxis cytokines), and matrix metalloproteases (MMPs). This constitutes neuro-inflammation, and microglia activation institutes the initial vital neuro-inflammatory response in acute stroke, which together with blood-borne innate immune cells and later adaptive immune cells support the course. This neuro-inflammatory response supposedly aims to reduce injury processes but this response under stroke pathology develops improperly more reactive and aggressive to yield numerous inflammatory mediators that trigger apoptosis and orchestrate lethal neuronal injury [47, 48]. Activated microglia becomes phagocytes that can release plethora of substances, some of which are neuroprotective such as neurotropic factors; nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), insulin-like growth factor I (IGF-I), and growth associated protein (GAP-43/B-50), while some are neurotoxic such as tumor necrosis alpha (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Blood–brain barrier (BBB) which confers brain with protection against systemic toxins is disrupted by matrix metalloproteinases (MMPs) with MMP-2 (gelatinase A) and MMP-9 (gelatinase B) being the leading concerns in cerebral ischemia [49]. MMP-2 that is normally expressed at low levels becomes increased during cerebral ischemia to galvanizes MMP-9, which abolishes components of the basement membrane in the vascular wall leading to BBB distraction, thus allowing further infiltration of inflammatory mediators and other potential toxins [50].
Oxidative stress signifies disparity in the high-level oxidants (free radicals) with respect to corresponding nonconforming low level of antioxidants. Long term cerebral hypo-perfusion produces abnormal proportions of reactive oxygen species (ROS) and/or reactive nitrogen species (RNS) oxidants through several mechanisms of injury, such as mitochondrial inhibition, calcium ions overload, ischemia–reperfusion injury, and neuroinflammation [51]. During cerebral ischemia, there is mitochondrial inhibition of oxidative phosphorylation due to the lack of sufficient oxygen, and the oxygen depleted cell shift to glycolytic pathway of ATP generation that results in lactate and hydrogen ion (H+) build-up in the mitochondria and the consequent reversal of the H+ uniporter on the mitochondrial membrane that results in superfluous cytosolic H+ buildup and acidosis [52]. Acidosis partly lead to oxidative stress by supplying excessive H+ for the successive progression in the generation of hydrogen peroxide (H2O2) and the final hydroxyl radicals (∙OH) either in the turnout of transition metal ions (Fenton reaction) or in the presence of superoxide radical (Haber-Weiss reaction), with this effect more pronounced in neurons due to inherently low anti-oxidant defense. In addition, the compelling protein and lipid oxidant peroxynitrite (OONO_) of RNS is favorably generated in the oxygen depleted cell by the reaction of nitric oxide (NO) and superoxide (O2∙−), thereby also contributing to oxidative stress.
Calcium overloads, as a result of glutamate mediated NMDA receptor excitotoxicity, also contributes in neuronal oxidative stress at cytosolic and mitochondrial level. At cytosolic level, excessive calcium ion activation of key intracellular enzymes such as neuronal nitric oxide synthase (nNOS) via Ca2+ binds calmodulin to induce subsequent downstream effect, as nNOS catalysis results in generation of nitric oxide (NO) free radical from L-arginine [53, 54]. At the mitochondrial level, excessive calcium ion influx into mitochondrial matrix leads to the inner mitochondrial accumulation of momentous level of Ca2+ via mitochondrial calcium uniporter (MCU) which proliferates disturbance of usual bio-energetic, mitochondrial ROS, and membrane permeability [55].
Apoptosis is a physiological mechanism of cell death through programmed cellular machinery of either extrinsic or intrinsic pathways [56]. Under stroke pathology, neuronal demise by necrosis preponderance in the ischemic prenumbra is marked by excitotoxicity, while additional process of neuronal demise by apoptosis which is more delayed and predominant in the ischemic penumbra occur in a fashion where apoptosis becomes dysregulated [57]. Thus, while the neurons within the core infarct die by immediate necrosis due to insufficient ATP, the penumbra die by ATP requiring process of apoptosis, supporting the established evidence that cellular demise after cerebral ischemia transpires through both necrosis and apoptosis [58]. Multiple pre-existing pathophysiologic mechanisms that can induce apoptosis after cerebral ischemia includes pro- calcium influx, pro-inflammatory cytokines and oxidative stress [59]. Apoptosis can be caspase-dependent or caspase-independent, and the most common is caspase-dependent which is initiated and triggered through distinctively intrinsic (or mitochondrial) pathway or extrinsic (or death receptor) pathway. Both intrinsic and extrinsic pathways share similar terminal phase termed execution phase where caspase 3 leads to the destruction of cellular components and cell death [60].
In hemorrhagic stroke, the mechanism of damage begins with additional process of mass effect from the mass accumulated blood, and cytotoxicity from the secondary metabolites of blood components, in addition to shared common damaging caused by ischemia such as excitotoxicity, neuroinflammation, oxidative/nitrosative stress, and apoptosis. The initial bleed from the cerebral hemorrhage causes immediate physical disruption of the cellular cytoarchitecture of the brain and increases local pressure which can cause compressions, hypothetically disrupting blood flow and principally causing brain herniation [61]. The subsequent expansion of hematoma causes mass effect of hematoma growth leading to further rise in intracranial pressure, brain herniation, and impacted blood flow that is correlated with neurologic deterioration and degraded clinical outcomes. Depending on the dynamic of hematoma expansion (growth), the primary damage ensues within minutes to hours subsequent to the onset of bleeding and is basically due to mechanical damage associated with the mass effect [62].
Secondary injury after cerebral hemorrhage termed as cytotoxicity occurs due to series of events initiated by the prior primary injury mechanism (mass effect), that is specifically due to body response to the hematoma for instance inflammatory response, and from the multiple blood components released from hematoma [61]. The extravasated blood components released from hematoma being implicated to cumulatively imposed cellular toxicity includes; majorly the erythrocytes and plasma proteins, and the damage-associated molecular patterns (DAMPs) which are nucleic acids, extracellular matrix components, proteins, lipid mediators, ATP and uric acid released from necrotic tissues [63]. At the early stage of cytotoxicity, the toxicity of extravasated blood plasma components such as coagulation factors, complement components, and immunoglobulins are known to be the main contributing factor of cellular damage. Subsequently, erythrocytes lysis leads to release of its major intracellular component hemoglobin (Hb), which when metabolize via hemoglobin metabolic pathway release degradation products; heme and iron (Fe). Both Hb and its degradation products are potent cytotoxic chemicals capable of causing death to many brain cells through mechanism of free radical generation with substantial increase oxidative stress and subsequent damage to DNA [62].
The ultimate goal of stroke management is to promote optimal recovery of lost functions and reduce further injury. This recovery depends majorly on brain plasticity; a spontaneous regeneration process that encompasses neural plastic changes in the lesioned hemisphere to reestablish its structural and functional reorganization. Brain plasticity under pathological condition completely differs from plasticity under properly functioning brain. For instance, plasticity in normally functioning brain is a prerequisite basis of learning and memory that involves plastic adaptation such as long-term potentiation (LTP). This is opposed to plastic changes observed using MRI in cerebral stroke pathology, that involves modification in intracortical myelin, augmented neurogenesis, improved spine density in neuronal dendrites and alterations in astrocyte volume [64].
Stroke recovery to certain extent also depends on severity extent of the initial injury deficit as the severity of the damage is inversely related to the prognosis for recovery [65]. But it was also observed that recovery differs even among post stroke patients with similar clinically assessed severity. This apparently stress the recovery role of other brain endogenous survival mechanism such as extent to which collateral circulation bypass to supply blood to the perilesional neurons, angiogenesis, inhibitory neurotransmitters that counteract excitotoxicity, and multiple representations of the same function in different cortical areas [66]. Appropriate rehabilitation and drug treatment that target underline cause of stroke are also critical to recovery after post stroke cerebral damage. Rehabilitation aims to maximize optimum recovery of lost functions as a result of impairments deficit after stroke but overall, brain plasticity underlies recovery promoted by rehabilitation [67, 68, 69].
Recovery from stroke has also been attributed to be dependent on resolution of early local processes in the brain that includes resolve of perilesional edema, re-emergence of circulation within the ischemic penumbra, resolution of remote functional depression of neurological function induced by process of diaschisis [70]. As previously stated stroke recovery majorly depends on brain reorganization process of plasticity which in turn dictates recovery promoted by rehabilitation. Mechanism through which rehabilitation mediates brain plasticity to promote recovery has been studied and explained. Rehabilitation such as physical therapists stroke interventions modifies neurotrophic factor expression in the CNS especially brain derived neurotrophic factor (BDNF), which in turn upon binding with its tyrosine kinase B (TrkB) cognate receptor recruits a cascade of signaling pathways that ultimately mediates activity-associated plasticity of neurons [71, 72]. Activity-associated plasticity signifies a means of functional and structural neuroplasticity that is tailored by the depolarizing behavior of neurons, and the mechanisms governing activity-associated plasticity includes LTP and activity-associated development of corticospinal circuitry among others [72]. Therefore, through brain plasticity after cerebral stroke, reorganization by recruiting cortical or subcortical structures to adopt the function of the injured tissue, reinforcement of remaining synaptic pathways and then creating new connections, recruitment of other pathways that are functionally alike the damaged tissue but anatomically distinct, strengthening of existing but weaker and functionally silent connections, can all be achieved to recover lost cerebral functions [73].
Neuroplasticity is a general term that covers all available processes of neuronal reorganization possible [66], such as neurogenesis, synaptogenesis, dendritic arborization, axonal sprouting, LTP, recruitment of other pathways, reinforcement of functionally silent synapses. Neurogenesis is the process of generating of neurons of neural cell types from precursors neural stem cells and/or neural progenitor cells (NPCs) [74]. Synaptogenesis is a broad term that encompasses the complex process of synaptic contacts formation, maturation and maintenance which form the basis for establishing neural circuits [75]. Dendritic arborization describes a process of neuronal dendrites tree-like branching out to make new synaptic connection through mechanisms of dendrite morphogenesis [76]. Sprouting is a form of plastic changes in the synapses in which there is axonal synaptic reorganization to modify the efficacy of synapses [77]. LTP is the fundamental form of synaptic plasticity where synapses become strengthened and this forms the cellular basis of learning and memory [78].
Neuroplasticity is regulated by the corresponding cascade of intracellular events that translates into plastic changes. However, the plastic changes may either be adaptive, where it is related with an upsurge in function or maladaptive where it is linked with adverse consequences such as loss of function or augmented damage [79, 80]. This brings about the concept that not all plasticity effect positively on clinical status, that maladaptive plastic changes from dysregulated neuroplasticity result in an aberrant neural organization [79]. Typical example of situation where neuroplasticity becomes maladaptive can be seen in new onset of seizures after long period of cerebral trauma, where aberrant progressive plastic changes in the brain in the form of inappropriate synaptogenesis and axonal sprouting accounts for this late development. Neuroplasticity can also be seen as structural where the plastic changes involves the organization and number of synapses such as synaptogenesis, axonal sprouting and dendritic arborization, or functional where the plastic changes involves the efficacy and strength of synaptic connections such as LTP.
The basis of plastic changes that allows for neuroplasticity to become realistic depend upon factors such as neuronal excitability, which define the ability of a nerve to produce an action potential and in turn depends on the permeability, electrical and chemical state of the neuron [81]. This is then followed by adaptive changes termed plasticity, in which there are stable functional transformations that occur in specific neuronal systems as a result of specific stimuli or the combination of stimuli [82]. Furthermore, it has been revealed that effective and repeated action potentials are required from the presynaptic neuron to stimulate the postsynaptic to cause a change in the strength of an interneuron connection [83]. Cumulatively, the aforementioned process leads to biochemical changes, and anatomical adaptations which reinforce the connections between neighboring neurons, thus accounting for molecular, cellular, systems, and behavioral perspectives of explaining neuroplasticity [84].
The strength of the excitation impulse must exceed the threshold value to increase the synaptic efficacy and the stability of the connections between neurons. Nevertheless, when neurons are stimulated only with subthreshold stimuli, the overall activity of the synapse may decrease [85]. Studies conducted on unilateral lesion of the hippocampus results in the formation of new synapses (synaptogenesis) by the axons from the remaining contra-lateral hippocampal system [86]. Thus, the postsynaptic portion of a synapse continues to function properly despite the degeneration of the presynaptic region, and the surviving axons form new synapses. The fibers that form the (new) synapses are homologous to the damaged synapses, which may significantly facilitate the restoration of normal function.
Table 1 summarized various strategies that were found to enhance neuroplasticity and the mechanism through which modulate neuroplasticity.
Strategy | Proposed mechanism reported to modulate and promote neuroplasticity | References |
---|---|---|
Transcranial direct current stimulation (noninvasive) | Modification of neuronal membrane potentials, consequently persuading neuronal excitability which form part of the basis of neuroplasticity. | [87, 88] |
Deep brain stimulation (invasive) | This by stimulating neuronal network connected to the stimulated region, the pathological neuronal network becomes altered by changes in the neurochemical components thereby inducing morphological changes in both the dendrites (dendritic arborization) and axons (axonal sprouting). | [89] |
Functional Electrical Stimulation (FES noninvasive) | Hypothesized to modulate neuroplasticity through repeated generation of neurons synaptic activity that might facilitate synaptic remodeling, leading to neural reorganization. | [90] |
Aerobic Exercise | Aerobic exercise is linked with surge in neurogenesis and angiogenesis, together with rise in neurotrophic molecules especially BDNF and other growth factors implicated in neurite outgrowth and synaptic plasticity | [91, 92] |
Brain-derived neurotropic factor (BDNF) therapy | By binding of BDNF to its TrkB cognate receptor, two distinctive intracellular signaling pathways namely phosphatidylinositol 3-kinase (PI3K)/Akt and mitogen-activated protein kinase/extracellular-signal-regulated kinase (MAPK/ERK) becomes initiated, thereby regulating transcriptional gene activity of neurite outgrowth and neurogenesis. | [93, 94] |
Statins | Proposed mechanism by which statins modulates neuroplasticity involves indirect effect through statin-mediated increase in proteins such as endothelial nitric oxide synthase (eNOS), vascular endothelial growth factor (VEGF), tissue plasminogen activator (tPA), and brain-derived neurotropic factor (BDNF) among others. | [95] |
Erythropoietin (EPO) therapy | EPO and EPO receptor (EPOR) that both becomes upregulated in response to cerebral ischemia, when supplemented act to indirectly augment neurogenesis through EPO-mediated increase in the expression vascular endothelial growth factor (VEGF) and brain-derived neurotropic factor (BDNF). | [96] |
Phosphodiesterase type 5 inhibitors (PDE-5 inhibitors) | PDE-5 inhibitors competitively inhibit phosphodiesterase enzymes responsible for converting cyclic guanylyl monophosphate (cGMP) back to GMP, thus fostering cGMP accumulation which has diverse cellular effect in the brain including angiogenesis, and neurogenesis which are requirements of neuroplasticity | [97] |
Vascular endothelial growth factor (VEGF) therapy. | Proposed mechanism through which VEGF modulates neuroplasticity involves mediating the PI3K–AKT–nuclear factor kappa B signaling pathway; an intracellular pathway that regulate transcriptional factors involves in neurogenesis | [98, 99] |
Various strategies that were found to enhance neuroplasticity.
Advancement in the understanding of mechanism of cerebral damage after stroke and brain neuroplasticity have continue to be a cutting-edge landmark information towards reducing human disability as a result of stroke. Strategies aimed at harnessing and augmenting neuroplasticity in complement with neurorehabilitation offers reasonable level of hope to maximize stroke recovery and diminish cerebral stroke induced neurological impairments. Although these strategies are rapidly evolving towards achieving clinical viability and success, more is needed to be done especially pertaining to outcome measures of neuroplasticity that rely on biomarkers of neuroplasticity rather than functional or behavioral outcome.
The authors declare no conflict of interest.
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